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Medical image segmentation, particularly in the context of ultrasound data,is a crucial aspect of computer vision and medical imaging. This paper delvesinto the complexities of uncertainty in the segmentation process, focusing onfetal head and femur ultrasound images. The proposed methodology involvesextracting target contours and exploring techniques for precise parametermeasurement. Uncertainty modeling methods are employed to enhance the trainingand testing processes of the segmentation network. The study reveals that theaverage absolute error in fetal head circumference measurement is 8.0833mm,with a relative error of 4.7347%. Similarly, the average absolute error infetal femur measurement is 2.6163mm, with a relative error of 6.3336%.Uncertainty modeling experiments employing Test-Time Augmentation (TTA)demonstrate effective interpretability of data uncertainty on both datasets.This suggests that incorporating data uncertainty based on the TTA method cansupport clinical practitioners in making informed decisions and obtaining morereliable measurement results in practical clinical applications. The papercontributes to the advancement of ultrasound image segmentation, addressingcritical challenges and improving the reliability of biometric measurements.

Uncertainty Modeling in Ultrasound Image Segmentation for Precise Fetal Biometric Measurements